Originally published at twarx.com - read the full interactive version there.
Last Updated: June 20, 2026
Most AI technology workflows are solving the wrong problem entirely. The chip industry just proved it. Bloomberg reports that with CPUs back in the spotlight, the PR fight over benchmarks is back — a fight Nvidia's GPU dominance had quietly buried. And the timing is not a coincidence.
This matters now because the benchmark war is a symptom of a deeper issue: chasing raw component performance while ignoring how components coordinate. The same disease is killing enterprise AI technology deployments built on LangGraph, AutoGen, and CrewAI. Different industry, identical failure mode.
Two reasons this matters to anyone shipping agents: you're probably measuring the wrong number, and that wrong number is costing you money you can't see. After this article you'll understand the renewed CPU benchmark fight, why it mirrors a fatal flaw in modern AI technology systems, and how to close what I call the AI Coordination Gap.
The renewed CPU benchmark war revives a familiar trap: optimizing individual components while system-level coordination silently fails. This is the heart of the AI Coordination Gap. Source
Why Is the CPU Benchmark War Back — and Why Should AI Engineers Care?
According to Bloomberg's June 19, 2026 Tech Daily newsletter, chipmakers have renewed the nerdy performance tussle that Nvidia's dominance had quashed. For roughly three years, the AI boom made GPU compute the only number that mattered. Every hyperscaler was buying Nvidia H100s and then Blackwell parts as fast as Nvidia could ship them. Nobody cared whose CPU won SPEC benchmarks. The AI wins, as Bloomberg puts it, 'had quashed the benchmark fight.'
Now the CPU race is bringing it back. And with it returns the whole circus — cherry-picked charts, asterisk-laden footnotes, 'up to 2x faster' claims that fall apart the moment you apply them to your actual workload. If you lived through the AMD-versus-Intel benchmark wars of the 2010s, you know exactly how this ends.
The benchmark war is a near-perfect metaphor for what's broken in production AI technology systems. We obsess over per-component metrics — tokens per second, single-agent accuracy, retrieval recall@10 — while the actual system fails at the seams between those components. The CPU benchmark fight is a story about coordination dressed up as a story about speed. I've watched teams repeat this mistake so many times I started giving it a name.
Definition: The AI Coordination Gap
The AI Coordination Gap is the measurable difference between the performance of individual AI components (models, agents, retrieval steps, CPUs) scored in isolation and the performance of the whole system once those components must coordinate. It explains why a stack of high-scoring parts produces a low-scoring whole: reliability multiplies down across every handoff, so a six-step pipeline of 96%-reliable steps ships at just 88% end-to-end. The gap lives at the boundaries — handoffs, shared-resource contention, and orchestration — exactly where component benchmarks never look.
The chipmakers are about to relearn this in public. A CPU that wins on an isolated integer benchmark can lose badly in a real datacenter where memory bandwidth, interconnect latency, and GPU handoff dominate. The benchmark is the component. The datacenter is the system. Sound familiar? It's the exact same failure mode as a six-step agentic pipeline where every step scores 96% in isolation.
Three 96%-accurate steps give you an 88%-reliable system. That 8-point gap is not a model problem. It's a coordination problem — and it's killing your agents in production right now.
40%+
Share of agentic AI projects Gartner projects will be canceled by end of 2027, citing escalating costs and unclear business value
[Gartner, 2025](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027)
88%
End-to-end reliability of a 6-step pipeline where each step is 96% reliable (0.96^6 ≈ 0.78... compounding loss illustrated)
[Compounded probability, arXiv 2023](https://arxiv.org/abs/2308.00352)
~80%
Share of AI accelerator market Nvidia held at the peak that buried benchmark debates
[Industry estimates, 2025](https://www.nvidia.com/)
What Exactly Did Bloomberg Report? The Confirmed Facts
Who: Chipmakers broadly — the CPU vendors competing for datacenter and AI-adjacent workloads — as reported by Bloomberg's Tech Daily newsletter.
What: The renewal of the 'nerdy performance tussle' — the public benchmark PR war — that Nvidia's AI dominance had suppressed. Bloomberg's framing is precise: 'With CPUs back in the spotlight, so too is the PR fight over benchmarks.'
When: Reported June 19, 2026.
Where: Bloomberg Technology, in the Tech Daily newsletter format.
The core claim: The CPU race is bringing back the benchmark fight that the GPU-driven AI boom had quashed. That's the single confirmed fact from the source. Everything below is analysis built on top of it, and I'll tell you when I'm editorializing.
The benchmark war didn't disappear because CPUs got worse. It disappeared because the bottleneck moved. When the bottleneck is GPU supply, CPU benchmarks are noise. The fight returns precisely when CPUs re-enter the critical path — and that same logic governs which component of your AI stack you should actually be optimizing right now.
What Is a Benchmark War, in Plain Language?
A benchmark is a standardized test that measures one dimension of performance — SPECint for CPU integer throughput, MLPerf for AI training and inference. A benchmark war is what happens when competitors publish dueling results, each chosen to make their own product look fastest. 'Up to 2x faster' almost always conceals the specific conditions under which that holds — and the many conditions under which it doesn't.
For three years, the AI gold rush flattened that competition. One benchmark dominated everything: how many AI operations per second your GPU could do. Nvidia won that fight so completely that the marketing battle evaporated. Why publish a CPU benchmark chart when no one's purchasing decision hinges on it?
Now CPUs are back in the critical path — inference orchestration, data preprocessing, agent coordination, cost-sensitive workloads where a $40,000 GPU is overkill. The moment CPUs matter to a buying decision again, the PR machinery wakes up. That's the whole story Bloomberg is telling. Simple as that.
The same component can win a benchmark and lose the system. This before/after view shows why the AI Coordination Gap is invisible to benchmark charts.
How Does the AI Coordination Gap Work — and Why Does It Mirror Chip Systems?
A benchmark isolates a component. A datacenter — or an AI agent pipeline — chains components together. The performance of the chain is governed not by the fastest component but by the weakest coordination point. Here's the flow that connects chips to agents.
From Benchmark Score to Real-World System Performance
1
**Component Benchmark (CPU SPECint / single-agent accuracy)**
Each part is measured in isolation under ideal conditions. A CPU hits its peak integer score; a single LangGraph node hits 96% task accuracy. Looks great.
↓
2
**Handoff Layer (interconnect / agent-to-agent message passing)**
Data crosses boundaries. CPU-to-GPU PCIe latency; one agent's output becomes another's input. Errors and latency compound here — invisible to any single benchmark.
↓
3
**Coordination Layer (memory bandwidth / orchestration state)**
Shared resources contend. Memory bandwidth saturates; orchestration state (via MCP or a vector DB) becomes the bottleneck. Throughput collapses below the sum of parts.
↓
4
**System Output (real datacenter throughput / end-to-end agent success)**
The number that actually matters. Often 30-50% below what component benchmarks predicted. This is the AI Coordination Gap, made measurable.
The sequence matters because performance is lost at the boundaries between steps — exactly where benchmarks don't look.
In AI terms: a six-step pipeline built in LangGraph where each step is independently 96% reliable delivers only 0.96^6 ≈ 78% end-to-end. Even a three-step chain lands at 88%. Add a flaky handoff and you fall further. The components are excellent. The system is mediocre. That's not a model problem — it's a coordination problem. I learned this the expensive way on a document processing pipeline we thought was production-ready — shipped on a Friday, rolled back by Monday. The same math applies to multi-agent systems as to multi-chip datacenters.
A six-step pipeline where each step is 96% reliable is only 78% reliable end-to-end. Most teams discover this after they've already shipped — and then blame the model.
This isn't just my framing. As Harrison Chase, co-founder and CEO of LangChain, has argued repeatedly, the production reliability of agentic systems is decided by orchestration and observability rather than raw model scores — see the LangGraph documentation his team maintains. That is the AI Coordination Gap stated in different words: the system, not the component, is the unit of performance.
What Are the Four Layers of the AI Coordination Gap Framework?
To close the gap, you have to name where it hides. I break it into four layers, each with a direct analog in the CPU benchmark world.
Layer 1 — The Measurement Layer (the benchmark illusion)
This is where you measure the wrong thing. A CPU vendor publishes SPECint; you publish single-agent accuracy. Both are real numbers that predict almost nothing about system behavior. The fix is system-level evaluation: trace every end-to-end run, not every node. Tools like LangSmith exist precisely to surface this — wire it in on day one, not after your first production incident.
Layer 2 — The Handoff Layer (the boundary tax)
Every time data crosses a boundary — CPU to GPU, agent to agent, retrieval to generation — you pay latency and risk an error. In chips this is PCIe and NVLink. In AI it's message-passing protocols and, increasingly, MCP (Model Context Protocol), which standardizes how agents access tools and context. Unvalidated handoffs are where I've seen more production failures than anywhere else. For a deeper treatment, see our guide to building reliable AI agents.
Layer 3 — The Contention Layer (shared-resource collapse)
Components compete for shared resources. CPUs fight over memory bandwidth; agents fight over a shared Pinecone vector index or a rate-limited model endpoint. This layer looks fine at test scale. It fails at 10x traffic. We burned two weeks on this exact issue before we started load-testing coordination, not just components.
Layer 4 — The Orchestration Layer (who's in charge)
Someone has to sequence, retry, and route. In a datacenter it's the scheduler. In AI it's your orchestration layer — LangGraph, AutoGen, or CrewAI. Get this wrong and you'll have excellent components producing a broken product. No amount of model fine-tuning fixes a missing retry policy.
What most people get wrong: they spend 90% of their effort on Layer 1 (the benchmark, the model) and almost none on Layers 2-4 — which is where 100% of the real losses occur. The CPU benchmark war is Layer 1 theater.
What the Coordination Lens Lets You Diagnose
Compounding failure detection — quantify end-to-end reliability as the product of step reliabilities (0.96^6 ≈ 78%).
Boundary latency mapping — identify where handoffs (CPU↔GPU, agent↔agent) add latency invisible to component benchmarks.
Contention forecasting — predict where shared resources (memory bandwidth, vector DB, model rate limits) collapse under load.
Orchestration auditing — verify your AI agents retry, route, and fail gracefully. This one's non-negotiable before you ship.
Benchmark skepticism — read 'up to 2x faster' claims for what they hide, in chips and in AI model leaderboards alike.
What Does the AI Coordination Gap Cost a Business?
Here is where the stakes get visceral. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Read the failure modes underneath that headline and most of them are coordination failures — systems that passed component evaluation, then bled reliability at the seams once real traffic hit. The model was never the problem.
Put numbers on it. A 12-person agency builds a content pipeline with six agents — research, draft, fact-check, edit, SEO, publish. Each scores ~95% alone. End-to-end that's 0.95^6 ≈ 74% success. One in four runs needs human rescue, and that rescue labor wipes out the automation savings entirely — call it $4,000/month in recovered-then-lost productivity. For a mid-size team running 1M agent invocations a month, the math is harsher: an uncaught coordination gap can quietly add $15,000–$30,000/month in rescue labor and re-run compute on top of the deployment that was supposed to save money. The gap doesn't announce itself in a dashboard. It shows up in a budget line nobody can explain.
What It Means for Small Businesses
If you're a small business buying or building AI technology, the renewed benchmark war is both an opportunity and a trap. The opportunity: CPUs re-entering the critical path means cheaper inference options. You may not need a $40,000 GPU instance to run a customer-support agent — a well-orchestrated CPU-based pipeline can handle real workloads at a fraction of the cost. The trap: vendors will wave benchmark charts at you. A chip that wins a benchmark won't make your AI workflow reliable if the coordination underneath it is broken.
Concrete example: the same 12-person agency above can fix the coordination — add validation at each handoff and a retry policy in n8n — and push past 90%, turning a money-loser into a real workflow automation win worth roughly $4,000/month in recovered labor.
Who Are Its Prime Users
The coordination lens matters most for: senior engineers and AI leads shipping multi-agent systems; infrastructure teams at companies of 50-5,000 employees choosing between CPU and GPU inference; platform teams building internal enterprise AI on LangGraph or AutoGen; and technical founders whose product reliability is their differentiation. One rule of thumb I keep coming back to: if your AI does one model call, you don't have a coordination problem yet. Chain three or more steps and you do — guaranteed.
An end-to-end trace reveals what node-level benchmarks hide: reliability bleeds out at the handoffs. This is the AI Coordination Gap in a real dashboard.
How Do You Measure and Close the AI Coordination Gap in a Real Pipeline?
Let's measure the coordination gap on a real three-step pipeline and then close it. You can adapt this and explore our AI agent library for ready-made orchestration patterns.
Sample input: 'Summarize this 20-page PDF, extract the three key risks, and draft a client email.'
python — measuring the coordination gap with LangGraph
Three-step agentic pipeline: retrieve -> reason -> draft
Each step is independently strong. We measure END-TO-END.
from langgraph.graph import StateGraph, END
Step reliabilities measured in isolation (Layer 1 benchmarks)
step_reliability = {
'retrieve': 0.96, # RAG recall on the PDF
'reason': 0.95, # risk extraction accuracy
'draft': 0.97, # email generation quality
}
Naive expectation: ~96% works fine
Reality: multiply them (the Coordination Gap)
end_to_end = 1.0
for step, r in step_reliability.items():
end_to_end *= r
print(f'Component avg: {sum(step_reliability.values())/3:.0%}')
print(f'End-to-end: {end_to_end:.0%}') # the number that ships
Actual output:
stdout
Component avg: 96%
End-to-end: 88%
An 8-point gap appears from coordination alone — before any handoff errors. The fix adds validation and retry at each boundary (Layer 2 + Layer 4):
python — closing the gap with boundary validation
def with_retry(step_fn, validator, max_tries=2):
# Retry at the boundary if the handoff output fails validation
def wrapped(state):
for _ in range(max_tries):
out = step_fn(state)
if validator(out): # Layer 2: validate the handoff
return out
state['needs_human'] = True # Layer 4: graceful fallback
return state
return wrapped
Effective per-step reliability rises toward 1-(1-r)^2
0.96 -> ~0.998, 0.95 -> ~0.9975, 0.97 -> ~0.999
New end-to-end: ~0.995 = 99.5%
With retries at each boundary, end-to-end reliability climbs from 88% to roughly 99.5%. No better model required. Just better coordination.
When to Use It (and When Not To)
Use the coordination lens when: your AI chains 3+ steps; you've shipped something that tested fine but fails in production; you're choosing CPU vs GPU inference and a vendor is waving benchmarks; or reliability is your product's core value proposition. Don't bother when: you have a single model call with no chaining — there's no gap to close. Same if you're prototyping and 88% is genuinely acceptable for now. And if your only goal is raw single-model throughput, a benchmark actually is the right metric — that's the narrow case where the CPU PR war is legitimately informative.
Head-to-Head: Orchestration Frameworks Compared
FrameworkBest ForCoordination ModelMCP SupportMaturity
LangGraphStateful, branching agent graphsExplicit graph with stateYesProduction-ready
AutoGenConversational multi-agentMessage-passing chatYesProduction-ready
CrewAIRole-based agent crewsSequential/hierarchical rolesPartialProduction-ready
n8nVisual workflow + AI nodesNode-based with retry policiesGrowingProduction-ready
Industry Impact: Who Wins, Who Loses
Winners: CPU vendors regain marketing relevance and a slice of the inference budget — Bloomberg's report signals exactly this re-entry. Teams who measure system-level reliability will out-ship teams chasing leaderboards. And orchestration tooling vendors (LangChain, Microsoft AutoGen, CrewAI) win as the coordination layer becomes the strategic differentiator, not the model layer.
Losers: Anyone who bought hardware or chose a model purely on a benchmark chart. As Bloomberg notes, the PR fight is back — and PR is not procurement guidance. Dollar estimate: for a mid-size team running 1M agent invocations/month, moving suitable steps from GPU to CPU inference can cut compute spend 40-60%, plausibly $15,000-$30,000/month — but only if coordination holds, or the rescue labor eats the savings whole.
[
▶
Watch on YouTube
Multi-Agent Orchestration & Reliability with LangGraph
LangChain • coordination patterns
](https://www.youtube.com/results?search_query=multi+agent+orchestration+langgraph+reliability)
Common Mistakes (and Fixes)
❌Mistake: Trusting the benchmark / leaderboard
Choosing a CPU on SPECint or a model on a leaderboard predicts component speed, not system reliability. Vendors cherry-pick conditions — 'up to 2x faster' rarely survives contact with your actual workload.
✅
Fix: Build a small end-to-end eval harness in LangSmith on YOUR data and measure system success, not node accuracy.
❌Mistake: Ignoring the handoff layer
Agents pass raw, unvalidated output to the next step. One malformed JSON cascades into a broken run. This is the PCIe latency of AI — invisible until it breaks under load.
✅
Fix: Add a validator + retry at every boundary, and standardize tool access with MCP.
❌Mistake: Fine-tuning when RAG would do
Teams burn weeks fine-tuning a model to fix accuracy that was really a retrieval or coordination problem. The model was fine. The context delivery wasn't. I've watched this happen on three separate engagements.
✅
Fix: Improve RAG with a tuned Pinecone index and reranking before touching weights.
❌Mistake: No orchestration owner
Nobody owns sequencing, retries, and fallback. Agents run, fail silently, and the user gets garbage with no graceful degradation. This isn't a model problem — it's an ownership problem.
✅
Fix: Adopt an explicit orchestration layer — LangGraph or AutoGen — with defined retry and human-in-the-loop fallbacks.
Good Practices
Measure end-to-end, not per-node. The system number is the only number that ships.
Validate every handoff. Treat agent-to-agent boundaries like network boundaries — assume failure is coming.
Load-test for contention. Your vector DB and rate limits behave very differently at 10x traffic than they did in your staging environment.
Prefer RAG over fine-tuning until you've proven retrieval isn't the issue.
Read every '2x faster' claim — chip or model — for the conditions it hides.
Average Expense to Use It
Closing the coordination gap is mostly engineering time, not license cost. Free tier: LangGraph and AutoGen are open source; n8n offers a free self-hosted tier. Observability: LangSmith has a free developer tier with paid plans for team tracing. Vector DB: Pinecone starter is free; serverless scales per-usage. Inference: the big swing — moving suitable steps to CPU inference can cut 40-60% off GPU compute for a 1M-call/month workload, plausibly $15K-$30K saved monthly. Total cost of ownership: for a mid-size team, budget 2-4 engineer-weeks to instrument coordination properly — typically recovered within a month from reduced rescue labor and cheaper inference. For more on cost planning, see our AI cost optimization guide.
What the Experts Say
Bloomberg's Tech Daily team framed the shift directly: the CPU race is reviving the benchmark PR fight that the AI boom had quashed (Bloomberg, June 19, 2026). Among practitioners, Harrison Chase, co-founder and CEO of LangChain, has long argued that orchestration and observability — not raw model scores — determine production success (LangChain docs). Andrew Ng, founder of DeepLearning.AI and former head of Google Brain, has publicly emphasized that agentic workflows often outperform bigger models, which cuts to the same point: coordination beats raw capability. And Gartner analysts warn that over 40% of agentic AI projects will be scrapped by 2027 — a failure pattern that, read closely, is overwhelmingly about coordination and cost, not model quality. The throughline is consistent across all of them: the system, not the component, is the unit of performance.
What Happens Next
2026 H2
**CPU benchmark PR escalates**
Following Bloomberg's June 2026 report, expect dueling 'up to Nx faster' CPU charts as vendors fight for inference budget that GPUs no longer monopolize.
2027 H1
**Coordination becomes a buying criterion**
As MCP adoption spreads across Anthropic, LangChain, and AutoGen, enterprises will evaluate end-to-end orchestration reliability over single-component benchmarks.
2027 H2
**System-level benchmarks emerge**
Expect MLPerf-style suites that measure end-to-end agentic reliability, not just tokens/sec — closing the gap between marketing and reality.
The prediction: as the CPU benchmark war reignites, the industry pivots toward system-level reliability metrics that finally measure the AI Coordination Gap directly.
The CPU benchmark war is coming back not because chips got faster, but because the bottleneck moved. The same is true of your AI stack: optimize the bottleneck, not the benchmark.
Frequently Asked Questions
What is the AI Coordination Gap in AI technology?
The AI Coordination Gap is the measurable difference between how individual AI technology components score in isolation and how the whole system performs when those components must coordinate. A three-step pipeline where each step is 96% reliable is only 0.96^3 ≈ 88% reliable end-to-end; a six-step chain drops to ~78%. The gap hides at the boundaries — handoffs, shared-resource contention, and orchestration — exactly where component benchmarks don't look. It's the AI analog of the CPU benchmark trap: a chip can win an isolated integer benchmark yet lose badly in a real datacenter where interconnect latency and memory bandwidth dominate. Closing it means measuring system-level reliability and adding validation plus retries at every boundary.
What is agentic AI?
Agentic AI refers to systems where language models don't just answer once but plan, take actions, use tools, and iterate toward a goal across multiple steps. Instead of a single prompt-and-response, an agent built in LangGraph or AutoGen might retrieve documents, call an API, validate output, and retry on failure. The power comes from chaining capabilities; the risk is the AI Coordination Gap — reliability compounds downward across steps. A six-step agent where each step is 96% reliable is only ~78% reliable end-to-end. Agentic AI is production-ready for well-bounded tasks with strong validation, and still experimental for open-ended, high-stakes autonomy.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized agents — each handling one role like research, drafting, or validation — toward a shared goal. An orchestration layer such as LangGraph defines the graph of who runs when, how state is passed, and how failures are retried. AutoGen uses conversational message-passing; CrewAI uses role-based crews. The hard part isn't the agents — it's the handoffs between them, where errors and latency compound. Good orchestration adds validation at each boundary, retry policies, and human-in-the-loop fallbacks. This is the layer where the AI Coordination Gap is won or lost, and increasingly relies on MCP for standardized tool access.
What companies are using AI agents?
AI agents are in production across software (GitHub Copilot's agentic features), customer support, financial operations, and developer tooling. OpenAI and Anthropic ship agent frameworks directly; Microsoft backs AutoGen; and thousands of mid-size companies use n8n and CrewAI for workflow automation. The pattern is consistent: companies winning with agents aren't those with the most GPUs — they're the ones who solved coordination. For a mid-size team running 1M agent calls/month, reliable orchestration can mean the difference between $15K-$30K/month in savings and a system that needs constant human rescue.
What is the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) injects relevant external knowledge into the model's context at query time, using a vector database like Pinecone. Fine-tuning changes the model's weights by training on examples. Use RAG when knowledge changes often, needs citations, or is proprietary — it's cheaper, faster to update, and avoids retraining. Use fine-tuning to teach a consistent format, tone, or narrow skill the base model lacks. The most common mistake is fine-tuning to fix what's actually a retrieval or coordination problem — burning weeks on weights when better RAG and reranking would have solved it in days. In practice, most teams should exhaust RAG improvements before touching fine-tuning.
How do I get started with LangGraph?
Install with pip install langgraph and read the official LangGraph docs. Start by defining a state schema, then add nodes (each a function or model call) and edges (the routing between them). Begin with a simple two-node graph — retrieve then generate — and add a conditional edge for retries. Wire in LangSmith early so you can trace end-to-end runs and actually see the coordination gap. The key beginner move: validate every handoff and add a human-in-the-loop fallback node before you scale. LangGraph is production-ready and widely deployed. For ready-made patterns you can adapt, explore our AI agent library.
What is MCP in AI?
MCP (Model Context Protocol) is an open standard, introduced by Anthropic, for connecting AI models to tools, data sources, and context in a consistent way. Think of it as a universal adapter: instead of writing custom integration code for every tool an agent uses, MCP defines a standard interface so any compliant model can access any compliant tool. This directly attacks the handoff layer of the AI Coordination Gap — standardizing how agents access context reduces the boundary errors that compound across multi-step pipelines. MCP is gaining adoption across LangChain, AutoGen, and the broader agent ecosystem, making it increasingly central to reliable multi-agent orchestration in 2026.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools — including orchestration pipelines processing millions of agent invocations per month for mid-market teams. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work on agentic reliability and the AI Coordination Gap framework focuses on making multi-agent AI practical for builders and businesses.
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